Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Jung, Soyi; Yun, Won Joon; Kim, Joongheon; Kim, Jae Hyun

doi:10.3390/electronics10050543

Cited by 12 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A deep reinforcement learning strategy for coordinated multi-agent unmanned aerial vehicle (UAV) systems is presented in [31] In order to achieve energy awareness in UAV-based big-data platforms, which are used for data collection and analysis in large-scale environments, the authors propose a system that employs deep reinforcement learning. The system is intended to reduce the amount of energy used by multiple UAVs for data collection and analysis.…”

Section: Machine Learning Techniques In Drone Energy Managementmentioning

confidence: 99%

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Haque¹,

Chowdhury²,

Hassanalian³

2023

Preprint

View full text Add to dashboard Cite

The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.

show abstract

Section: Machine Learning Techniques In Drone Energy Managementmentioning

confidence: 99%

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Haque¹,

Chowdhury²,

Hassanalian³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Another major challenge is energy supply and the limited range for UAVs. Jung et al [63] apply a solution based on CommNet [26] to coordinate the assignment of UAVs to charging towers and to share energy between towers, optimizing power draw from the electrical grid and minimizing operational costs.…”

Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

“…Most research on UAVs uses custom, low-fidelity environments to simplify development [61], [63]. However, there are multiple high-fidelity simulators available that model UAV dynamics in more detail.…”

Section: Unmanned Aerial Vehiclesmentioning

confidence: 99%

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

M.¹,

Brosig²,

Plinge³

et al. 2022

Preprint

View full text Add to dashboard Cite

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that only utilize single, independent agents leave potential performance gains on the road. Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other. This work aims to give an overview of the field to researchers in autonomous mobility. We first explain MARL and introduce important concepts. Then, we discuss the central paradigms that underlie MARL algorithms, and give an overview of state-of-the-art methods and ideas in each paradigm. With this background, we survey applications of MARL in autonomous mobility scenarios and give an overview of existing scenarios and implementations.

show abstract

“…Model-based fault-detection [16,28,29] Perception-actioncommunication loops [7,25] Independent Dec-POMDPs [46] Networking Dec-POMDPs [47,48] Transition dynamics or task requirements…”

Section: Communication Communication Noise Local Communication Rangementioning

confidence: 99%

Resilient robot teams: a review integrating decentralised control, change-detection, and learning

Bossens,

Ramchurn,

Tarapore

2022

Preprint

View full text Add to dashboard Cite

Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams. Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics. Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.

show abstract

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Cited by 12 publications

References 40 publications

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

Resilient robot teams: a review integrating decentralised control, change-detection, and learning

Contact Info

Product

Resources

About